This reviews the public second edition book by Richard Sutton and Andrew Barton on "Reinforcement learning".

This document serves as an expanded second edition of a book on reinforcement learning (RL), significantly updating the prior version from 1979. It organizes RL concepts into three main parts: tabular solution methods, function approximation for larger problems, and the interdisciplinary connections of RL with psychology and neuroscience. Key areas covered include dynamic programming (DP), Monte Carlo methods, temporal-difference (TD) learning, on-policy and off-policy learning, and gradient-based methods like policy gradients. The text provides a comprehensive overview of algorithms, theoretical underpinnings, and real-world applications such as game playing (e.g., AlphaGo) and system control, emphasizing the evolution of the field and areas for future research like safe RL and automated task selection.

http://incompleteideas.net/book/RLbook2020.pdf